Laboratory of Interfaces and Advanced Materials (LIMA), University of Monastir, Tunisia
Received date: October 13, 2015; Accepted date: November 12, 2015; Published date: November 20, 2015
Citation: Mabrouk S, Braham Y, Barhoumi H, Maaref A (2015) Characterization and Classification of Different Tunisian Geographical Olive Oils using Voltammetric Electronic Tongue. J Food Process Technol 7:534. doi:10.4172/2157-7110.1000535
Copyright: © 2015 Mabrouk S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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In this work, we describe a sensor based on glassy carbon electrode, employed to discriminate between olive oils from different Tunisian regions. The characterization was made using three electrochemical techniques, cyclic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). Each type of oil provides a diversity of characteristic signals that can be used as an input variable of different statistics analysis, like principle component analysis (PCA), cluster analysis (CA) and discriminate factorial analysis (DFA).The results resulting from the electrochemical methods are compared. The obtained results show the reliability of the used methods on the discrimination between olive oil qualities obtained from different regions of Tunisia.
Olive oils; Tocopherols; Electronic tongue; Voltammetry measurements; Principal components Analysis; Discriminate factorial analysis
The quality of liquids is usually evaluated by several methods. Among these methods we can cite, chromatographic techniques, chemical analysis and mass spectrometry. In fact, none of the techniques mentioned above cannot be considered as a method of measurement completely characterizing the degree of freshness or bitterness of the food industry, where the need to search for new techniques to assess handle the reliable product quality in the food industry. In short, the food industry is looking for a technique that allows a practical, rapid, reliable and inexpensive to determine the state of freshness of products and analyze complex liquid samples. In consists an array of nonspecific sensors coupled to a pattern recognition technique (E- tongue) [1-11]. The “electronic tongue” is a set (array) of partially selective chemical sensors with sensitivity to as wide a number of solution components as possible. Combined with multivariate analysis and/or pattern recognition programs such a system may provide quantitative information about multiple components of liquids and be used for recognition or identification of a liquid [12-24].
It is well- known that the virgin olive oils content different several classes of compounds having antioxidant proprieties such as polyphenols, tocopherols, carotenoids, sterols . Such proprieties are the reason of the health benefits and protect oil from autooxidation [26, 27]. In this work, a novel method has been developed to discriminate between olive oils picked from different geographical regions and of different bitterness degrees. The objective of this work is to develop a novel electrochemical method to discriminate between the olive oils by means of an array of voltammetric sensors based on carbon paste. The global response of six olive oils has been recorded by three electrochemical techniques such as cyclic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV)). The capability of discrimination of the olive oils has been analyzed by Principal Component Analysis (PCA), Cluster Analysis (CA) and Discriminate Factorial Analysis (DFA) of the obtained cyclic voltammograms.
Dichloromethane and tetrabutylammonium tetraphenylborate were purchased from Sigma-Aldrich. Six different geographical virgin olive oils (VOOs) varieties were picked from Tunisia regions.
Glassy carbon is an interesting electrode material because it is inexpensive and has a rich surface chemistry can be exploited to influence its reactivity. Also, its large range of potential provides a good survey of electrochemically. A glassy carbon electrode of 3 mm in diameter was used. The geometric surface area was 0.071 cm2 and an Ag/AgCl/KCl (sat) as reference electrode.
Insulator glassy carbon electrode cleaning
The working electrode was treated by a polishing powder with alumina and rinsed between each polishing step by ultrapure water then sonicated for 10 min. After each measurement, electrodes were washed with dichloromethane. The glassy carbon electrode was activated electrochemically in solution of H2SO4 (0.5M) with oxygen. A series of 5 cycles from 1.8 to -0.5 V at scan rate of 100 mVs-1 vs.
Electrochemical characterizations and instrumentation
Voltammetric electronic tongue set up and measurements: Electrochemical experiments were performed using a potentiostat Autolab PG30 electrochemical analyzer. The software with a conventional three-electrode cell including an Ag/AgCl/KCl (Sat) as the reference electrode, a platinum electrode as the counter electrode and the glassy carbon as working electrode. The electrodes were immersed in the electrolytic solutions and the responses were measured for four times. After each measurement, electrodes were washed with dichloromethane. Electrochemical measurements were performed in 2 mL of olive oils solutions using 10 mL of dichloromethane and 0.02 g of tetrabutylammonium tetraphenylborate. Three electro analytical techniques were used: Voltammetry cyclic (VC), Differential pulse voltammetry (DPV) and Square wave voltammetry (SWV). Cyclic Voltammograms (VC) were registered from -1 to 3V at a scan rate of 100 mv/s. Differential pulse voltammetry (DPV) was performed at the same potential range by using a modulation time 0.02s, in interval time of 0.1s, step potential 0.0025 V and modulation amplitude 0.02 V. The square wave voltammetry (SWV) was performed at frequency of 15 Hz, amplitude of 0.09 V and potential step of 25 mV.
Pattern Recognition Methods
A pattern recognition statistical method was used for the evaluation and classification of the signals. The principal component analysis (PCA), differential factorial analysis (DFA) and cluster analysis (CA) were carried out using the software Spss 18. In this work we show a comparative study between PCA and DFA for the classification of the olive oils samples in the training set. PCA is an unsupervised learning technique that allows reduction of multidimensional data to a lower dimensional approximation, while simplifying the interpretation of the data by the first and second principal components (PC1 and PC2) in two dimensions and preserving most of the variance in the data. In addition, the samples can be classified without prior information on the samples. Conversely, DFA requires prior knowledge about the samples during the training. DFA is a supervised learning technique, which classifies the sample by developing a model and then identifies the unknown samples .
The voltammetric measurements
After cleansing glassy carbon electrode step and immersion in dichloromethane, the treated electrode was employed to classify the six olive oils by three electro-analytical techniques cyclic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). The cyclic voltammograms of several olive oils immersed in glassy carbon electrode showed a variety of responses. Figure 1 show three anodic peaks at -0.7 V, 0.6 V and 1.7 V. Figure 1 illustrates the cyclic voltammetry (CV) curves obtained using the glassy carbon sensors immersed in olive oils. Peaks associated to the polyphenolic content on the oils under study could be observed in the 0.1-0.8 V region (Table 1).
Table 1: The cyclic voltammetry parameters associated to the polyphenolic content on the oils.
The main differences between curves consist in the position (potential), form and intensity of the peaks.
The Figure 2, of DPV voltammograms confirms the observations of the cyclic voltammetry. And shows the DPV responses recorded in six extra virgin olive oils coming from different Tunisian regions. For each oil sample, mixed with electrolyte support and dichloromethane as above, six replicates were collected. The patterns displayed in Figure 2 with full lines are average currents against potential plots, obtained from at least five replicates, while the shadow regions represent the signal variability range of each type of sample in both forward and backward scans of each measurement. It must be remarked that the hysteresis observed in each scan is due to the fact that, in these highly viscous media, planar diffusion affects to some extent the mass transport to the microelectrode surface . As is evident from Figure 2, although the voltammograms obtained for the different olive oils have a similar shape, the associated current values differ significantly from one another, especially in the cathodic zone (Table 2). This is possibly due to a different composition of the oil matrix.
Table 2: DPV voltammograms parameters associated to the polyphenolic content on the oils.
The peaks observed in the SWV voltammograms (Figure 3) translated the proprieties of the electro active coumpounds, antioxidants present in the olive oils. Figure 3 shows that these oils were produced from the cultivars Kasserine, WEd Elil, Seliana, Mahdia, Ben Guerdne and Sidi Bouzid , respectively. In this case, the current circle in the anodic region, which was recorded reproducibly only in this oil, are a peculiar characteristic of this type of sample that allows distinguishing between all oils (Table 3).
Table 3: SWV voltammograms parameters associated to the polyphenolic content on the oils.
The electronic tongue data
The characteristics of different samples are treated via pattern recognition techniques, such as the Principal Component Analysis (PCA) and Factorial Discriminant Analysis (FDA) .
PCA voltammetric electronic tongue: The discrimination capability of the method was evaluated by means of principal component analysis of the obtained signals. In order to reduce the high number of variables contained in a voltammetric curve to a few representative ones, a method was developed in our laboratory that uses mathematical functions that capture the information along the dynamic characteristics of the global response . In this work we used a glassy carbon electrode for discrimination of vegetable oils. Figure 4 shows the PCA plot obtained (PC 2 vs. PC 1) of E-tongue results on Tunisian VOOs taste, and illustrates each measurement with the 20 normalized variables. As one can see, the variances explained by the first and the second principal components are 99.66%. However, in this plot, there was a certain area of overlapping in which no clear differentiation could be made on Oil sidi bouzid and Oil wed elil. Also, the analysis was extended until the third axis, but no great improvement can be revealed.
Electronic tongue with differential pulse voltammetry data: Figure 5 shows the PCA plot obtained (PC 2 vs. PC 1) for E-tongue differential pulse voltammetry results on Tunisian VOOs taste and describes each measurement with the 20 normalized variables. We can see the variances explained by the first component (96.853%) and the second principal component 2.554%. But, in this plot there was a certain area of overlapping in which clear differentiation could be made on Oil_sidi bouzid, Oil_wed elil, Ben Guerdene and Kasserine. We note that the fingerprint oils from the region Sidi Bouzid, Mahdia and kasserine are situated in the right of the first component. That explains that these cities are located in the middle. The data clusters that belong to different simples were separated from each other. The first component tends to group the data according to their different regions of sample. Thus, the most olive oils are located on the right side, and the other finger print is localized on the left side. The second component seems to differentiate the different simple according to their area overlapping.
Electronic tongue with square wave voltammetry data: Figure 6 shows the PCA plot obtained (PC 2 vs. PC 1) for E-tongue. PC1 explains 99.673% variances and PC2 explains 0.283% variances. We can clearly see that two PC axes contribute potentially in the separation of the six Tunisian VOOs. Consequently, PCA results show a perfect classification between all the studied VOOs by square wave voltammetry.
FDA plot of different varieties of olive oil by voltammetric electronic tongue: DFA is a multivariate technique for describing a mathematical function that will distinguish among predefined groups of samples. As an eigenvalue-eigenvector method, DFA has a strong connection to multiple regression and principal components analysis.
Applying FDA, a good separation between VOOs samples was obtained (Figure 7a). Function 1 and Function 2 appeared to contribute largely to discriminate mostly between all olive oil varieties. Through the DFA (Figure 7a), the first two factors explain 99.8% of the data variability, showing the distribution of the class separation between the dimensions, and a slight enhancement in the classification and separation among different categories is obtained. Although a clear differentiation was evident between the olive oils from different regions sample. However, after analyzing the same data set with DFA each group was clearly distinguishable (Figure 7b). The two discriminate functions accounted for 99.8% of the variance, indicating that the separated result is better with DFA (supervised method) than that with PCA (unsupervised method). We have observed a good separation between the Tunisian olive oil by DFA analysis, as results, 99.4% of the samples was successfully classified. However, after analyzing the same dataset with DFA each group was clearly distinguishable (Figure 7c). The two discriminate functions accounted for 100% of the variance, indicating that the separated result is better with DFA (supervised method) than that with PCA (unsupervised method). Therefore, a better separation of the olive oils data was achieved by means of the DFA, demonstrating that DFA is better than PCA particularly when the number of samples is high producing a data overlapping in the clusters .
A novel method has been developed for the characterization of several olive oils cultivated from different region in Tunisia, by different voltammetric techniques. These methods are based on glassy carbon electrode. The features observed in the voltammograms are reflecting of properties of the electro-active compounds (antioxidants) present in the olive oils. The signal can be used as input variables in statistical studies. Principal component analysis using voltammetric signals fingerprint in multivariate data analysis allows a clear discrimination among oils pick from Tunisian geographic plot. The E-Tongue using square wave voltammetry are the best method to separate these olive oils. The characterization by different methods (VC, DPV and SWV) shows that the intensities of current differ from oil to another. The classification success rate obtained using a SWV (99.961%) is similar as CV (99.56%) and DPV (98.407%). A clearly obvious discrimination is found between all sources of olive oils by ACP. The voltammetry electronic tongue is a reliable tool to distinguish olive oil collected from different regions in Tunisia. We have observed good separations between the Tunisian olive oil by DFA analysis, as results, 100% of the samples were successfully classified.
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